Abstract

Emotion Recognition using EEG signals remains a challenging task. Usually, feature extraction and channel selection are determined based on neuro-scientific assumptions. Too many features during the EEG-based human emotion recognition will lead to reduced classification accuracy and consume high computational costs. This study analyzes time and frequency domain features such as Mean, Mean Absolute Value, Standard Deviation, and Power Spectral Density. In this study, an EEG Recording session involved 25 subjects consisting of 12 males and 13 females. Video with two emotions, happy and sad, were stimulated to the subjects. The electrodes were placed in channels F7, F8, FP1, and FP2 based on the 10/20 EEG system. The EEG pre-processing, such as signal filtering, Automatic Artifact Removal EOG, Artifact Subspace Reconstruction, and Independent Component Analysis, were done using MATLAB Toolbox, followed by Infinite Impulse Response with Butterworth was applied to separate the EEG signal into alpha, beta, and gamma sub-band. Therefore, 48 numbers of features were extracted to perform emotion recognition. Mutual Information is used for calculating the degree of importance of each feature. Then, the features were ranked to eliminate features with a minimal contribution. We implemented a Random Forest algorithm to classify human emotions based on the EEG signal. The experimental results show that reducing the number of utilized features from 48 to 12 can increase the accuracy score from 82.61 % to 95.65 %.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
EditorsMochammad Facta, Mohammad Syafrullah, Munawar Agus Riyadi, Imam Much Ibnu Subroto, Irawan Irawan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-24
Number of pages6
ISBN (Electronic)9786239213558
DOIs
Publication statusPublished - 2022
Event9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022 - Jakarta, Indonesia
Duration: 6 Oct 20227 Oct 2022

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2022-October
ISSN (Print)2407-439X

Conference

Conference9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
Country/TerritoryIndonesia
CityJakarta
Period6/10/227/10/22

Keywords

  • EEG
  • Emotion Recognition
  • Feature Selection
  • Mutual Information
  • Random Forest

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